In today's environment, many public as well as private areas around the world are being continuously or intermittently captured by surveillance devices such as cameras and in particular video cameras.
However, it is practically impossible to fully cover a large area, such that all people or objects in the area are captured at every given moment, and there may exist gaps between the fields of view captured by different cameras, such as between cameras capturing areas along a road.
“Automated Multi-Camera Surveillance: Algorithms and Practice” by Omar Javed and Mubarak Shah. Springer, 2008, and U.S. Pat. No. 7,450,735 to Shah et al. disclose tracking and surveillance methods and systems for monitoring objects passing in front of non-overlapping cameras. The documents disclose finding corresponding tracks from different cameras and working out which object passing in front of the camera(s) made the tracks, in order to track the object from camera to camera. The documents disclose the usage of an algorithm to learn inter-camera spatial temporal probability using Parzen windows, and the inter-camera appearance probabilities using distribution of Bhattacharyya distances between appearance models, to establish correspondences based on Maximum A Posteriori (MAP) framework combining both spatial temporal and appearance probabilities, and to update learned probabilities throughout the lifetime of the system.
“Bayesian multi-camera surveillance” by V. Kettnaker and R. Zabith in International Conference on Computer Vision and Pattern Recognition, pages 117-123, 1999, discloses that the task of multi-camera surveillance is to reconstruct the paths taken by all moving objects that are temporarily visible from multiple non-overlapping cameras. A Bayesian formalization of this task is disclosed, where the optimal solution is the set of object paths with the highest posterior probability given the observed data. It is shown how to efficiently approximate the maximum a posteriori solution by linear programming.
“Object identification in a Bayesian context” by T. Huang and S. Rusell, in Proceedings of IJCAI, 1997 discloses that object identification—the task of deciding that two observed objects are in fact one and the same object—is a fundamental requirement for any situated agent that reasons about individuals. Object identity, as represented by the equality operator between two terms in predicate calculus, is essentially a first-order concept. Raw sensory observations, on the other hand, are essentially propositional, especially when formulated as evidence in standard probability theory. The paper describes patterns of reasoning that allow identity sentences to be grounded in sensory observations, thereby bridging the gap. We begin by defining a physical event space over which probabilities are defined. An identity criterion is introduced which selects those events that correspond to identity between observed objects. From this, we are able to compute the probability that any two objects are the same, given a stream of observations of many objects. It is shown that the appearance probability, which defines how an object can be expected to appear at subsequent observations given its current appearance, is a natural model for this type of reasoning. The theory is applied to the task of recognizing cars observed by cameras at widely separated sites in a freeway network, with new heuristics to handle the inevitable complexity of matching large numbers of objects and with online learning of appearance probability models.